A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

Development and validation of a novel risk assessment model for accurate prediction of intraoperative hypothermia in adult patients undergoing different types of surgery: insights from a multicentre, retrospective cohort study

BACKGROUND

Intraoperative hypothermia is a prevalent complication that may significant clinical and economic burdens. Previous risk assessment models have demonstrated limitations in accurately predicting intraoperative hypothermia, particularly in diverse surgical populations. This study aims to develop and validate a model in adult surgical patients to improve outcomes.

METHODS

This retrospective cohort study utilized data extracted from electronic medical records and anaesthesia information management systems between June 2022 and August 2023. The analysis included information of 3,405 adult surgical patients from three independent centres in China who underwent elective surgical procedures with body temperature monitoring. Intraoperative hypothermia was defined as a core temperature below 36 °C during surgery. The Least Absolute Shrinkage and Selection Operator (LASSO) regression employed to select optimal features and multivariate logistic regression was used to identify independent predictors of intraoperative hypothermia and then built the risk assessment model. We further evaluated the discriminative ability, calibration curves, and clinical utility of the predictive model.

RESULTS

The total incidences of intraoperative hypothermia in adult surgical patients were 42.5%. The predictors in the intraoperative hypothermia model included: age, BMI, baseline HR, baseline temperature, minimally invasive surgery, smoking, previous surgery and serum creatine level. In the training cohort, the model demonstrated strong discriminatory ability, with C-index values of 0.721 (95% CI 0.697-0.744). Internal and external validation further confirmed the model's robustness and generalizability.

CONCLUSION

These findings suggest that our model may help us more accurately identify patients at risk of intraoperative hypothermia.

TRIAL REGISTRATION

China Clinical Trial Registration Center (ChiCTR2300071859), Date registered May/26/2023.

Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

OBJECTIVE

This study aims to systematically review and critical evaluation of the risk of bias and the applicability of existing prediction models for acute kidney injury post liver transplantation.

DATA SOURCE

A comprehensive literature search up until February 7, 2024, was conducted across nine databases: PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, CNKI, Wanfang, CBM, and VIP.

STUDY DESIGN

Systematic review of observational studies.

EXTRACTION METHODS

Literature screening and data extraction were independently conducted by two researchers using a standardized checklist designed for the critical appraisal of prediction modelling studies in systematic reviews. The prediction model risk of bias assessment tool was utilized to assess both the risk of bias and the models' applicability.

PRINCIPAL FINDINGS

Thirty studies were included, identifying 34 prediction models. External validation was conducted in seven studies, while internal validation exclusively took place in eight studies. Three models were subjected to both internal and external validation, the area under the curve ranging from 0.610 to 0.921. A meta-analysis of high-frequency predictors identified several statistically significant factors, including recipient body mass index, Model for End-stage Liver Disease score, preoperative albumin levels, international normalized ratio, and surgical-related factors such as cold ischemia time. All studies were demonstrated a high risk of bias, mainly due to the use of unsuitable data sources and inadequate detail in the analysis reporting.

CONCLUSIONS

The evaluation with prediction model risk of bias assessment tool indicated a considerable bias risk in current predictive models for acute kidney injury post liver transplantation.

IMPLICATIONS FOR CLINICAL PRACTICE

The recognition of high bias in existing models calls for future research to employ rigorous methodologies and robust data sources, aiming to develop and validate more accurate and clinically applicable predictive models for acute kidney injury post liver transplantation.

A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation

BACKGROUND

This study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx).

METHOD

A total of 229 LTx patients were enrolled, and genotyping for 153 single nucleotide polymorphisms (SNPs) was performed. The cohort was randomly divided into training (n = 183) and validation (n = 46) sets in an 8:2 ratio. Statistically significant SNPs identified through pharmacogenomic analysis were combined with clinical factors to construct a comprehensive prediction model for persistent AKI using multivariate logistic regression analysis. Discrimination and calibration analyses were conducted to evaluate the performance of the model. Decision curve analysis was used to assess its clinical utility. Due to the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.

RESULTS

The final nomogram comprised nine predictors, including body mass index, thrombin time, tacrolimus initial concentration, rs757210, rs1799884, rs6887695, rs1494558, rs2069762 and rs2275913. In the training set, the area under the receiver operating characteristic curve of the nomogram was 0.781 (95%CI: 0.715-0.846), while in the validation set it was 0.698 (95%CI: 0.542-0.855), indicating good model fit. As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Additionally, decision curve analysis confirmed its clinical applicability.

CONCLUSION

This study presents a genotype-guided nomogram that can be used to assess the risk of persistent AKI following LTx and may assist in guiding personalized prevention strategies in clinical practice.

Intraoperative hypotension during critical phases of liver transplantation and its impact on acute kidney injury: a retrospective cohort study

INTRODUCTION

Acute Kidney Injury (AKI) following Liver Transplantation (LT) is associated with prolonged ICU and hospital stay, increased risk of chronic renal disease, and decreased graft survival. Intraoperative hypotension is a modifiable risk factor associated with postoperative AKI. We aimed to determine in which phase of LT hypotension has the strongest association with AKI: the anhepatic or neohepatic phase.

METHODS

This retrospective cohort study included adult patients undergoing LT between January 2010 and June 2022. Exclusion criteria were re-do or combined transplantations, preoperative dialysis, and early graft failure or death. Primary outcome was AKI as defined by KDIGO. Hypotension was Mean Arterial Pressure (MAP) below predefined thresholds in minutes. Risk adjusted logistic regression analysis considered hypotension in 3 periods: the total procedure, anhepatic phase, and neohepatic phase.

RESULTS

Our cohort included 1153 patients. The median MELD-NA score was 19 (IQR 11-28), and 412 (35.9%) were living-related donations. AKI occurred in 544 patients (47.2%). The unadjusted model showed an association with AKI for MAP < 60 mmHg (OR = 1.011 [1.0, 1.022], p = 0.047) and MAP < 55 mmHg (OR = 1.023 [1.002, 1.047], p = 0.040) in the anhepatic phase, and for MAP < 60 mmHg (OR = 1.032 [1.01, 1.056], p = 0.006) in the neohepatic phase. The adjusted model did not reach significance in the subgroups but did in the total procedure: MAP < 60 mmHg (OR = 1.005 [1.002, 1.008], p < 0.001) and MAP < 55 mmHg (OR = 1.008 [1.003-1.013], p = 0.004).

CONCLUSION

Intraoperative hypotension is independently associated with AKI following LT. This association is seen during the anhepatic phase. Maintaining MAP above 60 mmHg may improve kidney function after LT.

Rosiglitazone attenuates Acute Kidney Injury from hepatic ischemia-reperfusion in mice by inhibiting arachidonic acid metabolism through the PPAR-γ/NF-κB pathway

BACKGROUND

Acute Kidney Injury (AKI), a prevalent complication of Liver Transplantation (LT) that occurs during the perioperative period has been established to profoundly impact the prognosis of transplant recipients. This study aimed to investigate the mechanism of the hepatic IRI-induced AKI and to identify potential therapeutic targets for treating this condition and improving the prognosis of LT patients.

METHODS

An integrated transcriptomics and proteomics approach was employed to investigate transcriptional and proteomic alterations in hepatic IRI-induced AKI and the hypoxia-reoxygenation (H/R) model using TCMK-1 cells and the hepatic IRI-induced AKI mouse model using male C57BL/6 J mice were employed to elucidate the underlying mechanisms. Hematoxylin-eosin staining, reverse transcription quantitative polymerase chain reaction, enzyme-linked immunosorbent assay and Western blot were used to assess the effect of Rosiglitazone (RGZ) on hepatic IRI-induced AKI in vitro and in vivo.

RESULTS

According to the results, 322 genes and 128 proteins were differentially expressed between the sham and AKI groups. Furthermore, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analyses revealed significant enrichment in pathways related to amino acid and lipid metabolism. Additionally, the Protein-Protein Interaction (PPI) network analysis of the kidney tissues obtained from a hepatic IRI-induced AKI mouse model highlighted arachidonic acid metabolism as the most prominent pathway. Animal and cellular analyses further revealed that RGZ, a PPAR-γ agonist, could inhibit the expression of the PPAR-γ/NF-κB signaling pathway-associated proteins in in vitro and in vivo.

CONCLUSIONS

These findings collectively suggest that RGZ ameliorates hepatic IRI-induced AKI via PPAR-γ/NF-κB signaling pathway modulation, highlighting PPAR-γ as a crucial therapeutic target for AKI prevention post-LT.

A multi-center study to predict the risk of intraoperative hypothermia in gynecological surgery patients using preoperative variables

OBJECTIVES

Hypothermia is highly common in patients undergoing gynecological surgeries under general anesthesia, so the length of hospitalization and even the risk of mortality are substantially increased. Our aim was to develop a simple and practical model to preoperatively identify gynecological surgery patients at risk of intraoperative hypothermia.

METHODS

In this retrospective study, we collected data from 802 patients who underwent gynecological surgery at three medical centers from June 2022 to August 2023. We further allocated the patients to a training group, an internal validation group, or an external validation group. The preliminary predictive factors for intraoperative hypothermia in gynecological patients were determined using the least absolute shrinkage and selection operator (LASSO) method. The final predictive factors were subsequently identified through multivariate logistic regression analysis, and a nomogram for predicting the occurrence of hypothermia was established.

RESULTS

A total of 802 patients were included, with 314 patients in the training cohort (mean age 48.5 ± 12.6 years), 130 patients in the internal validation cohort (mean age 49.9 ± 12.5 years), and 358 patients in the external validation cohort (mean age 47.6 ± 14.0 years). LASSO regression and multivariate logistic regression analyses indicated that body mass index, minimally invasive surgery, baseline heart rate, baseline body temperature, history of previous surgery, and aspartate aminotransferase level were associated with intraoperative hypothermia in gynecological surgery patients. This nomogram was constructed based on these six variables, with a C-index of 0.712 for the training cohort.

CONCLUSIONS

We established a practical predictive model that can be used to preoperatively predict the occurrence of hypothermia in gynecological surgery patients.

CLINICAL TRIAL REGISTRATION

chictr.org.cn, identifier ChiCTR2300071859.

Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

BACKGROUND

Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI.

METHODS

A total of 120 patients were eligible for inclusion in the study. Clinical information was extracted from the institutional electronic medical record system. Blood samples were collected prior to surgery and immediately after surgery. Univariable and multivariate logistic regression were used to identify independent risk factors. Finally, a nomogram was developed based on the final multivariable logistic regression model.

RESULTS

In total, 58 (48.3%) patients developed AKI. Multivariable logistic regression revealed four independent risk factors for post-LT AKI: operation duration [odds ratio (OR) = 1.728, 95% confidence interval (CI) = 1.121-2.663, p = 0.013], intraoperative hypotension (OR = 3.235, 95% CI = 1.316-7.952, p = 0.011), postoperative cystatin C level (OR = 1.002, 95% CI = 1.001-1.004, p = 0.005) and shock (OR = 4.002, 95% CI = 0.893-17.945, p = 0.070). Receiver operating characteristic curve analysis was used to evaluate model discrimination. The area under the curve value was 0.815 (95% CI = 0.737-0.894).

CONCLUSION

The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.